Figure 1.
Overview of power flow in V2G [8] and the DC-DC converter.
Electric vehicles (EVs) are an emerging technology that contribute to reducing air pollution. This paper presents the development of a 200 kW DC charger for the vehicle-to-grid (V2G) application. The bidirectional dual active bridge (DAB) converter was the preferred fit for a high-power DC-DC conversion due its attractive features such as high power density and bidirectional power flow. A particle swarm optimization (PSO) algorithm was used to online auto-tune the optimal proportional gain (KP) and integral gain (KI) value with minimized error voltage. Then, knowing that the controller with fixed gains have limitation in its response during dynamic change, the PSO was improved to allow re-tuning and update the new KP and KI upon step changes or disturbances through a time-variant approach. The proposed controller, online auto-tuned PI using PSO with re-tuning (OPSO-PI-RT) and one-time (OPSO-PI-OT) execution were compared under desired output voltage step changes and load step changes in terms of steady-state error and dynamic performance. The OPSO-PI-RT method was a superior controller with 98.16% accuracy and faster controller with 85.28 s-1 average speed compared to OPSO-PI-OT using controller hardware-in-the-loop (CHIL) approach.
Citation: Suliana Ab-Ghani, Hamdan Daniyal, Abu Zaharin Ahmad, Norazila Jaalam, Norhafidzah Mohd Saad, Nur Huda Ramlan, Norhazilina Bahari. Adaptive online auto-tuning using Particle Swarm optimized PI controller with time-variant approach for high accuracy and speed in Dual Active Bridge converter[J]. AIMS Electronics and Electrical Engineering, 2023, 7(2): 156-170. doi: 10.3934/electreng.2023009
| [1] | Vengadesan Alagapuri, Ashok Bakkiyaraj Radhakrishnan, S. Sakthivel Padaiyatchi . Optimization of position and rating of shunt and series connected FACTS devices for transmission congestion management in deregulated power networks. AIMS Electronics and Electrical Engineering, 2024, 8(2): 165-186. doi: 10.3934/electreng.2024007 |
| [2] | Mohamed M. Albarghot, Mohamed T. Iqbal, Kevin Pope, Luc Rolland . Dynamic modeling and simulation of the MUN Explorer autonomous underwater vehicle with a fuel cell system. AIMS Electronics and Electrical Engineering, 2020, 4(1): 114-131. doi: 10.3934/ElectrEng.2020.1.114 |
| [3] | Gabriele Corzato, Luca Secco, Alessio Vitella, Atulya Kumar Nagar, Emanuele Lindo Secco . E-Mobility: dynamic mono-phase loads control during charging session of electric vehicles. AIMS Electronics and Electrical Engineering, 2018, 2(2): 37-47. doi: 10.3934/ElectrEng.2018.2.37 |
| [4] | Saqib J Rind, Saba Javed, Yawar Rehman, Mohsin Jamil . Sliding mode control rotor flux MRAS based speed sensorless induction motor traction drive control for electric vehicles. AIMS Electronics and Electrical Engineering, 2023, 7(4): 354-379. doi: 10.3934/electreng.2023019 |
| [5] | José M. Campos-Salazar, Pablo Lecaros, Rodrigo Sandoval-García . Dynamic analysis and comparison of the performance of linear and nonlinear controllers applied to a nonlinear non-interactive and interactive process. AIMS Electronics and Electrical Engineering, 2024, 8(4): 441-465. doi: 10.3934/electreng.2024021 |
| [6] | Dipak R. Swain, Sunita S. Biswal, Pravat Kumar Rout, P. K. Ray, R. K. Jena . Optimal fractional sliding mode control for the frequency stability of a hybrid industrial microgrid. AIMS Electronics and Electrical Engineering, 2023, 7(1): 14-37. doi: 10.3934/electreng.2023002 |
| [7] | Shaswat Chirantan, Bibhuti Bhusan Pati . Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement. AIMS Electronics and Electrical Engineering, 2024, 8(2): 265-291. doi: 10.3934/electreng.2024012 |
| [8] | Santosh Prabhakar Agnihotri, Mandar Padmakar Joshi . Alternating current servo motor and programmable logic controller coupled with a pipe cutting machine based on human-machine interface using dandelion optimizer algorithm - attention pyramid convolution neural network. AIMS Electronics and Electrical Engineering, 2024, 8(1): 1-27. doi: 10.3934/electreng.2024001 |
| [9] | Thanh Tung Pham, Chi-Ngon Nguyen . Adaptive PID sliding mode control based on new Quasi-sliding mode and radial basis function neural network for Omni-directional mobile robot. AIMS Electronics and Electrical Engineering, 2023, 7(2): 121-134. doi: 10.3934/electreng.2023007 |
| [10] | Ameer L. Saleh, Fahad Al-Amyal, László Számel . Control techniques of switched reluctance motors in electric vehicle applications: A review on torque ripple reduction strategies. AIMS Electronics and Electrical Engineering, 2024, 8(1): 104-145. doi: 10.3934/electreng.2024005 |
Electric vehicles (EVs) are an emerging technology that contribute to reducing air pollution. This paper presents the development of a 200 kW DC charger for the vehicle-to-grid (V2G) application. The bidirectional dual active bridge (DAB) converter was the preferred fit for a high-power DC-DC conversion due its attractive features such as high power density and bidirectional power flow. A particle swarm optimization (PSO) algorithm was used to online auto-tune the optimal proportional gain (KP) and integral gain (KI) value with minimized error voltage. Then, knowing that the controller with fixed gains have limitation in its response during dynamic change, the PSO was improved to allow re-tuning and update the new KP and KI upon step changes or disturbances through a time-variant approach. The proposed controller, online auto-tuned PI using PSO with re-tuning (OPSO-PI-RT) and one-time (OPSO-PI-OT) execution were compared under desired output voltage step changes and load step changes in terms of steady-state error and dynamic performance. The OPSO-PI-RT method was a superior controller with 98.16% accuracy and faster controller with 85.28 s-1 average speed compared to OPSO-PI-OT using controller hardware-in-the-loop (CHIL) approach.
Global warming is a radically aggravating phenomenon due to many causes. One such cause is the burning of fossil fuels in line with the growing of energy consumption. Among the many approaches that have been implemented to mitigate the environmental problems, power electronics (PE), which is efficient in deploying energy, has been identified as the key player in producing green energy [1]. Progression of renewable energy (RE) and smart microgrid have instigated the growth of PE applications such as electric vehicles (EVs) and energy storage units [2].
The development of EVs was predicted to grow rapidly as have the features of clean technology [3]. Moreover, the implementation of RE sources in EVs has significantly reduced the dependency on fossil fuels [4]. Plug-in Hybrid Electric Vehicles (PHEV) is an example of a vehicle-to-grid (V2G), which is built-in with battery pack and grid connection [4,5]. Having said that, there are two types of EV chargers, namely the on-board charger and off-board charger, the AC and DC types, respectively [6]. This study, however, designed a system to be compatible with the off-board DC charger of CHAdeMO 1.2 type standard with 200 kW rated power and 400 A maximum current [7]. This study only considers the bidirectional DC-DC converter as depicted in Figure 1.
The bidirectional power flow in DC-DC converter acts as an interface between DC high voltage bus and EVs battery. The bidirectional power flow can be classified into a non-isolated and isolated structure. Based on the variable voltage conversion ratio and safety benefits, isolated configuration is usually chosen instead of the non-isolated structure [9,10]. Having said that, the dual active bridge (DAB) isolated DC-DC converter is most preferred in the energy storage system (ESS) applications [11,12].
DAB converter was introduced in the 1990s [13]. In DAB there are bidirectional power flow features that are it capable of storing energy during charging and the energy is sent back to the grid during the discharging mode [4,14]. Apart from that, the promising soft switching, high efficiency, high power density, simple control strategy and galvanic isolation of the DAB DC-DC converter made it popular recently [15,16].
The DAB system employs many optimization techniques. Among the available techniques, the particle swarm optimization (PSO) algorithm is the most widely accepted due to fewer parameters, high speed of convergence time and simple mathematical equation [17,18] compared to Lagrange Multiplier [19] and analytical method based on Karush–Kuhn–Tucker [20]. Several studies have proposed an offline method to determine optimal phase-shift angle, ϕ using PSO to minimize reactive power [21] and current stress [22]. Despite the advantages of the offline method such as reducing the computing cost [23], its implementation in real time has a downside whereby the parameter or setting needs to be adjusted in the prepared lookup table if there are any changes. Meanwhile, online optimization method in DAB using an analytical method based on global optima technique was developed to minimize backflow power [24] and root mean square (RMS) current [25].
On the other hand, the proportional-integral (PI) controller is a linear controller that is widely used in various applications, including power electronic applications due to its simple design and effectiveness [18]. PI controller is also used to regulate the output voltage in the DAB system. However, the PI controller tuning process has demerits in term of time consuming and stability guaranteed because of the trial-and-error method used and it only delivers satisfactory performance for narrow operating range [26]. Hence, PSO is the most preferred method for tuning and obtaining the optimal PI parameters [27,28,29] that results in less overshoot, low total harmonic distortion and improves the fast response.
Even though the offline and online tuning of PI using PSO have already been researched, the online auto-tuned PI controller using PSO algorithm have not been evaluated yet in DAB DC-DC converter in analyzing steady-state error, eSS and transient response in order to evaluate the system accuracy and speed's controller, respectively. Hence, this paper presents the development of DAB DC-DC converter using online auto-tuned by re-tuning features through the PSO-PI method. This method is crucial in the DAB system, especially in EV or transportation applications due to the system’s robustness and high dynamic response [30,31]. The re-tuning method was introduced to address the issue of the fixed gains restriction, which provided the same responses to the applied changes in dynamic system [32]. Moreover, recent research has incorporated artificial intelligence substantially in the use of EVs, such as voltage control strategy based on deep reinforcement learning [33] and multiagent reinforcement learning method [34]. Yet, the PSO algorithm is still relevant due to less computational methods and is efficient to be embedded in the system environments [18].
The online auto-tuned PSO-PI with re-tuning approach (OPSO-PI-RT) based on the single-phase-shift (SPS) modulation is proposed in this study and is compared with online auto-tuned PSO-PI with a one-time execution (OPSO-PI-OT) technique. The SPS modulation was chosen due to its competition efficient feature for the system design.
Section Ⅱ of this paper discusses the DAB system and SPS control. Section Ⅲ outlines the overview of the PSO and how PI is tuned using the stochastic optimization. The setup of Hardware-in-the-Loop (HIL) is detailed in Section Ⅳ. Meanwhile, the experimental results of the eSS and dynamic performance of the four methods are analyzed in Section Ⅴ. Lastly, Section Ⅵ concludes this study.
The DAB, as depicted in Figure 2, is a DC-DC converter of two active bridges. Both bridges are interfaced through a high frequency transformer. The leakage inductance, Lk, is the main element of energy transferring in the DAB. The equivalent topology of DAB converter is illustrated in Figure 3, where V1 is the voltage at primary side, V2 is the voltage at secondary side, ILk is the current of leakage inductance and VLk is the voltage of leakage inductance. The pulse width modulation (PWM) switching at each bridge is set to 50% of the duty cycle to enable both bridges to produce a symmetrical square-wave pulse with a ϕ value between the bridges. A constant switching frequency is used in the design.
SPS is one of the modulation strategies that have been applied in DAB. It is the simplest strategy that produces high efficiency when operated under narrow voltage variations, where voltage conversion ratio, k is close to one [35]. Despite the improvement of phase-shift modulation, such as extended phase-shift (EPS), dual phase-shift (DPS) and triple phase-shift (TPS), the SPS is still preferred as it is easy to implement and reliable [30,36,37].
SPS only needs to control one ϕ to control the magnitude and direction of power flow in DAB. The primary side leads to the second part in a forward direction and conversely in reverse power flow. The power transmission of DAB can be obtained using (1), where Ts denotes switching the interval. Following mathematical manipulations, the power flow can be expressed as (3), in which Vi represents input voltage, Vo output voltage, n transformer turns ratio and fs switching frequency.
| P=1Ts∫t4t0V1(t)iLk(t)dt | (1) |
| P=12π∫2π0V1(t)iLk(t)dt | (2) |
| P=nViVoϕ122πfsLk(1−ϕ12π);ϕ12=ϕ1−ϕ2 | (3) |
The maximum power can be calculated as (4), which occurs when ϕ is 90° (π2). The transformer turns ratio, n and voltage conversion ratio, k can be calculated using (5) and (6) respectively, where NP and NS represent the primary turn and secondary turn of the transformer correspondingly.
| Pmax=nViVo8fsLk | (4) |
| n=NPNS | (5) |
| k=nVoVi | (6) |
Figure 4 indicates the square waveform at each bridge and leakage inductance voltage, VLK when V1 = 750 V and V2 = 500 V with maximum ϕ at 90°. Only one ϕ need to be controlled in this system since the SPS modulation is used.
PSO is a stochastic algorithm based on the social behavior of bird flocking or fish schooling that was introduced in 1995 [39]. This swarm intelligence conducts a search process using a group of particles. Each particle represents a candidate solution to the given problem. Particles set with initial parameters will explore and exploit iteratively following the objective function until the desired outcome is achieved. In each iteration, the particles will update the particle best, Pbest, then global best, Gbest will be selected from the best of Pbest solution. The velocity and position of the particles involved are adjusted using (7) and (8) respectively.
| vi(k+1)=wvi(k)+c1r1[Pbesti−xi(k)]+c2r2[Gbest−xi(k)] | (7) |
| xi(k+1)=xi(k)+vi(k+1) | (8) |
where w is the inertia weight, c1 and c2 are the acceleration coefficient, r1 and r2 are the random number between 0 to 1, Pbest is the personal best position of particle i, Gbest is best position of the particles and k represents the iteration number.
In the proposed method, PI controller is used to regulate the ϕ as to control the desired Vo. Hence, optimal proportional gain (KP) and integral gain (KI) values using PSO will produce the suitable ϕ as to yield the desired Vo. The block diagram of the proposed method is illustrated in Figure 5.
The objective function is defined by the minimum root mean square error (RMSE) as shown in (9).
| RMSEmin=√1N∑Ni=1(Vref−Vo)2 | (9) |
The PSO parameter of w, c1, c2, r1 and r2 in the proposed method are fixed values. The three particles which represent the three pairs of KP and KI are used during the search process through time variant [40,41] as illustrated in Figure 6. Each particle is limited to 0.5 ms for each iteration. In the first iteration, the initial KP and KI values evaluate the objective function using (9). The fitness values of each particle are represented by the minimum RMSE of the DAB system. The KP and KI values with the best fitness values will represent Pbest for each particle. Then, the Gbest values were selected from the best fitness values of Pbest, with least minimum RMSE. This is followed by the new velocity and position of particles that are updated using (7) and (8) correspondingly. The new KP and KI produced the right ϕ as to generate the gate signal to all eight switches (S1 to S8) in trying to regulate the Vo. In the second iteration, the new Pbesti is compared with the old Pbesti, whereby a new Gbest is updated accordingly. Then, the velocity and position are also updated based on updated values. This process is repeated continuously until all the particles are converged at certain values. Finally, the appropriate ϕ is obtained from the optimal values of KP and KI, where the DAB system generates the desired output with minimum eSS voltage.
On the other hand, the system of the proposed method is updated with reset features, whereby the KP and KI values change according to the new load or desired Vo. When the step changes occurred, PSO algorithm was triggered to allow the re-tuning process. Then, the particles are initialized again at the initial position when all reset requirements, such as all particles must be in convergence state; error voltage, Ve must be greater and equal than certain values and all fitness values for each particle must converge with each other satisfactorily. Those requirements are vital to avoid the PSO from performing sudden re-tuning at the initial iteration while Ve is still large.
All particles continued with the optimization process as reflected in the new condition, with these procedures being iterated several times until reaching the termination criterion by assuming that no step-changes occurred again. However, if the step-change occurred for the second time before the termination and fulfilled the re-tuning principles, the re-tuning process will be repeated to regulate the output as desired. Then, the new KP and KI will be optimized following the previous steps to conceive a DAB system with minimum eSS. Therefore, with re-tuning ability, PSO can make new searches to adapt with new conditions to regulate new Vo. Figure 7 and Figure 8 depict the flowchart and overview of the working re-tuning in the proposed method, respectively.
Meanwhile, the one-time execution method (OPSO-PI-OT) follows a similar concept to OPSO-PI-RT. However, the difference lies during the step change or disturbances, where the KP and KI values in OPSO-PI-OT maintain and try to regulate the desired output with the old optimal PI values.
In real-time implementation, the algorithms of the DAB controller for all methods were implemented using Texas Instrument F28335 DSP card control. The hardware emulator, Typhoon-HIL 402 was used to develop the power circuit of the DAB converter. Moreover, a built-in field-programmable gate array (FPGA) in the Typhoon enables the modelling system to perform under a real-time form. Figure 9 presents the controller hardware-in-the-loop (CHIL) setup for a 200 kW DAB system designed using the parameters tabulated in Table 1. The Vo from DAB system in Typhoon-HIL is fed to the DSP card controller.
| Components | Values |
| Transformer turns ratio, n | 2 :1 |
| Rated power, Pmax | 200 kW |
| Input voltage, Vi | 750 V |
| Output voltage, Vo | 500 V |
| Leakage inductance, Lk | 24 µH |
| Switching frequency, fs | 20 kHz |
DownLoad:
CSV
The controller of the PSO algorithm and PI controller was implemented in MATLAB/Simulink (MATLAB Support Package for TI C2000 library) and loaded into the DSP board. Then, the ϕ that was produced was sent to the gate signal of the switches in DAB circuit through enhanced pulse width modulator (ePWM) module. The deadtime was set to 0.6 µs and the DAB converter operated at a fixed switching frequency of 20 kHz.
Table 2 presents the steady-state and transient response performances of the DAB converter during load step-change at 270 V, 375 V and 420 V. The eSS is measured during the steady-state response after applying the disturbance or step change to the system. The steady-state performance was initially evaluated by applying the load-step change during half load (100 kW) to full load (200 kW) condition. When the load was stepped-up from 100 kW to 200 kW at 270 V as depicted in Figure 10, the eSS of the OPSO-PI-OT has a higher percentage of eSS with 1.47% compared to OPSO-PI-RT. The proposed OPSO-PI-RT method yielded the least eSS with 1.39%, where it was 5.44% more accurate than OPSO-PI-OT. Besides, the best accuracy is exhibited by OPSO-PI-RT when the load was stepped at 375 V, where the accuracy was 5.56% superior to OPSO-PI-OT as illustrated in Figure 11. There is just a slight difference in values of eSS between OPSO-PI-OT and OPSO-PI-RT when the load was changed at 420 V, which OPSO-PI-RT only has 0.6% best performance than OPSO-PI-OT.
| Load Step-change | Vref (V) | Average eSS (%) | Average settling time (ms) | ||
| OPSO-PI-OT | OPSO-PI-RT | OPSO-PI-OT | OPSO-PI-RT | ||
| 100kW to 200kW | 270 | 1.47 | 1.39 | 15.80 | 15.60 |
| 375 | 1.44 | 1.36 | 13.70 | 13.20 | |
| 420 | 1.66 | 1.65 | 9.50 | 10.10 | |
| 150kW to 70kW | 270 | 4.13 | 3.20 | 10.50 | 10.05 |
| 375 | 1.54 | 1.57 | 9.90 | 10.80 | |
| 420 | 3.84 | 1.70 | 11.10 | 10.60 | |
DownLoad:
CSV
When the load was stepped-down from 150 kW to 70 kW at 270 V (k = 0.72), as presented in Figure 12, the proposed OPSO-PI-RT exhibited 22.52% better performance by producing the smaller ripple voltage with 3.20% of eSS relative to OPSO-PI-OT that produce 4.13% of eSS. As shown in Figure 13, the OPSO-PI-RT has a tremendous performance at 420 V by producing 55.73% better accuracy than OPSO-PI-OT. With that percentage, it shows that the tuned optimal KP and KI values at 150 kW in the OPSO-PI-OT method are not the ideal values for the 70 kW system. Thus, it shows the significant advantages of the proposed OPSO-PI-RT that allows KP and KI values to update continuously according to the changes and it can eliminate more ripple voltage compared to OPSO-PI-OT.
In summary, the comparative percentage of eSS between OPSO-PI-RT and OPSO-PI-OT indicate that the OPSO-PI-RT has good accuracy with 14.65% superior performance than OPSO-PI-OT. Thus, by updating the KP and KI optimal values for current system conditions has diminished the ripple in the DAB system and guarantees the proposed OPSO-PI-RT is more precise than OPSO-PI-OT in both load step conditions. It proves that the KP and KI obtained in this method change according to the new load and ensure better performance of the DAB system with minimal eSS.
The DAB converter was further analyzed by measuring the dynamic response in terms of settling time during the disturbances or step changes in the system. The settling time for OPSO-PI-OT was longer, taking 15.80 ms for the Vo to reach desired value compared to OPSO-PI-RT with 15.60 ms during the load change from 100 kW to 200 kW at 270 V as shown in Figure 10. From Figure 11, the OPSO-PI-OT recorded the settling time of 13.20 ms to regulate back to the desired value after the load change at 375 V, which was 0.5 ms faster than OPSO-PI-OT.
Furthermore, when the load was changed from 150 kW to 70 kW at 270 V, as demonstrated in Figure 12, it is visibly shown that OPSO-PI-RT gives 0.45 ms faster recovery during transient than OPSO-PI-OT by producing 10.05 ms of settling time. Besides, the settling time of OPSO-PI-RT was shorter compared to OPSO-PI-OT as shown in Figure 13, where the settling time was 11.10 ms and 10.60 ms for OPSO-PI-OT and OPSO-PI-RT, respectively.
This evaluation will determine the speed of the controllers where it refers to the inversion of the settling time as shown in (10).
| speed(s−1)=1000settlingtime(ms) | (10) |
As predicted, the proposed OPSO-PI-RT achieved the best speed even after going through the reset process during the step change or disturbance by producing 85.28 s-1 compared to OPSO-PI-OT with 85.10 s-1. The re-tuning in OPSO-PI-RT proved that it does not get distracted nor does it delay the system, and yet can still yield faster control than OPSO-PI-OT.
This paper proposed a bidirectional DAB DC-DC converter using SPS modulation. A 200 kW system with online auto-tuned PI using PSO algorithm was developed to improve system accuracy and improve the controller's speed. The main analyses, including CHIL results, were discussed for step change load disturbance at different voltage conversion ratios. The performance of the proposed OPSO-PI-RT method was compared with OPSO-PI-OT. The effectiveness of the proposed OPSO-PI-RT method was verified through CHIL experiments. It recorded least eSS of less than 4% in all conditions tested. The proposed OPSO-PI-RT exhibited the faster dynamic response with minimal settling time in regulating back to the desired value after experiencing step change or disturbances in the DAB system. With the noteworthy outcomes of high accuracy and high speed that the proposed controller realized, both features are being expected to give the better performance of high power EV charger application. With these improvements, the EVs will receive significant consideration from users as the charger technology would feature rapid charging and fast system response by maintaining its system accuracy.
The authors would like to thank the University Malaysia Pahang for providing financial support under an internal research grant (Grant Number: RDU220309).
All authors declare no conflicts of interest in this paper.
| [1] |
Bose BK (2010) Global warming: Energy, Environmental Pollution, and the Impact of Power Electronics. IEEE Ind Electron Mag 4: 6–17. https://doi.org/10.1109/MIE.2010.935860 doi: 10.1109/MIE.2010.935860
|
| [2] |
Khan SA, Islam R, Guo Y, Zhu J (2019) A New Isolated Multi-Port Converter With Multi-Directional Power Flow Capabilities for Smart Electric Vehicle Charging Stations. IEEE T Appl Supercon 29: 1–4. https://doi.org/10.1109/TASC.2019.2895526 doi: 10.1109/TASC.2019.2895526
|
| [3] |
Latifi M, Rastegarnia A, Khalili A, Sane S (2018) Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles. IEEE T Ind Electron 66: 3668–3680. https://doi.org/10.1109/TIE.2018.2853609 doi: 10.1109/TIE.2018.2853609
|
| [4] |
Richardson DB (2013) Electric vehicles and the electric grid : A review of modeling approaches, Impacts, and renewable energy integration. Renew Sustain Energy Rev 19: 247–254. https://doi.org/10.1016/j.rser.2012.11.042 doi: 10.1016/j.rser.2012.11.042
|
| [5] |
Rubino L, Capasso C, Veneri O (2017) Review on plug-in electric vehicle charging architectures integrated with distributed energy sources for sustainable mobility. Appl Energy 207: 438–464. https://doi.org/10.1016/j.apenergy.2017.06.097 doi: 10.1016/j.apenergy.2017.06.097
|
| [6] |
Ashique RH, Salam Z, Aziz MJ, Bhatti AR (2017) Integrated photovoltaic-grid dc fast charging system for electric vehicle : A review of the architecture and control. Renew Sustain Energy Rev 69: 1243–1257. https://doi.org/10.1016/j.rser.2016.11.245 doi: 10.1016/j.rser.2016.11.245
|
| [7] | Blech T (2019) CHAdeMO DC charging standard: evolution strategy and new challenges. |
| [8] |
Tan KM, Ramachandaramurthy VK, Yong JY (2016) Integration of electric vehicles in smart grid : A review on vehicle to grid technologies and optimization techniques. Renew Sustain Energy Rev 53: 720–732. https://doi.org/10.1016/j.rser.2015.09.012 doi: 10.1016/j.rser.2015.09.012
|
| [9] |
Sha D, Wang X, Chen D (2017) High-Efficiency Current-Fed Dual Active Bridge DC – DC Converter With ZVS Achievement Throughout Full Range of Load Using Optimized Switching Patterns. IEEE T Power Electron 33: 1347–1357. https://doi.org/10.1109/TPEL.2017.2675945 doi: 10.1109/TPEL.2017.2675945
|
| [10] |
Oggier GG, Garcia GO, Oliva AR (2009) Switching Control Strategy to Minimize Dual Active Bridge Converter Losses. IEEE T Power Electron 24: 1826–1838. https://doi.org/10.1109/TPEL.2009.2020902 doi: 10.1109/TPEL.2009.2020902
|
| [11] |
Waltrich G, Hendrix MA, Duarte JL (2015) Three-Phase Bidirectional DC / DC Converter With Six Inverter Legs in Parallel for EV Applications. IEEE T Ind Electron 63: 1372–1384. https://doi.org/10.1109/TIE.2015.2494001 doi: 10.1109/TIE.2015.2494001
|
| [12] |
Karthikeyan V, Gupta R (2017) Light-load efficiency improvement by extending ZVS range in DAB- bidirectional DC-DC converter for energy storage applications. Energy 130: 15–21. https://doi.org/10.1016/j.energy.2017.04.119 doi: 10.1016/j.energy.2017.04.119
|
| [13] |
De Doncker RW, Divan DM, Kheraluwala MH (1991) A Three-phase Soft-Switched High-Power-Density DC/DC Converter for High Power Applications. IEEE T Ind Appl 27: 63–73. https://doi.org/10.1109/28.67533 doi: 10.1109/28.67533
|
| [14] |
Zhang K, Shan Z, Jatskevich J (2017) Large- and Small-Signal Average-Value Modeling of Dual-Active-Bridge DC-DC Converter Considering Power Losses. IEEE T Power Electron 32: 1964–1974. https://doi.org/10.1109/TPEL.2016.2555929 doi: 10.1109/TPEL.2016.2555929
|
| [15] |
Park Y, Chakraborty S, Khaligh A (2022) DAB Converter for EV Onboard Chargers Using Bare-Die SiC MOSFETs and Leakage-Integrated Planar Transformer. IEEE T Transp Electr 8: 209–224. https://doi.org/10.1109/TTE.2021.3121172 doi: 10.1109/TTE.2021.3121172
|
| [16] |
Li L, Xu G, Xiong W, Liu D, Su M (2021) An Optimized DPS Control for Dual-Active-Bridge Converters to Secure Full-Load-Range ZVS With Low Current Stress. IEEE T Transp Electr 8: 1389–1400. https://doi.org/10.1109/TTE.2021.3106130 doi: 10.1109/TTE.2021.3106130
|
| [17] |
Deželak K, Bracinik P, Sredenšek K, Seme S (2021) Proportional-integral controllers performance of a grid-connected solar pv system with particle swarm optimization and ziegler–nichols tuning method. Energies 14: 2516. https://doi.org/10.3390/en14092516 doi: 10.3390/en14092516
|
| [18] |
Faisal SF, Beig AR, Thomas S (2020) Time Domain Particle Swarm Optimization of PI Controllers for Bidirectional VSC HVDC Light System. Energies 13: 866. https://doi.org/10.3390/en13040866 doi: 10.3390/en13040866
|
| [19] |
Hou N, Song W, Wu M (2016) Minimum-Current-Stress Scheme of Dual Active Bridge DC-DC Converter With Unified Phase-Shift Control. IEEE T Power Electron 31: 8552–8561. https://doi.org/10.1109/TPEL.2016.2521410 doi: 10.1109/TPEL.2016.2521410
|
| [20] |
Shi H, Wen H, Chen J, Hu Y, Jiang L, Chen G, et al. (2018) Minimum-Backflow-Power Scheme of DAB-Based Solid-State Transformer With Extended-Phase-Shift Control. IEEE T Ind Appl 54: 3483–3496. https://doi.org/10.1109/TIA.2018.2819120 doi: 10.1109/TIA.2018.2819120
|
| [21] |
Shi H, Wen H, Hu Y, Jiang L (2018) Reactive Power Minimization in Bidirectional DC – DC Converters Using a Unified-Phasor-Based Particle Swarm Optimization. IEEE T Power Electron 33: 10990–11006. https://doi.org/10.1109/TPEL.2018.2811711 doi: 10.1109/TPEL.2018.2811711
|
| [22] |
Hebala OM, Aboushady AA, Ahmed KH, Abdelsalam I (2018) Generic Closed Loop Controller for Power Regulation in Dual Active Bridge DC / DC Converter with Current Stress Minimization. IEEE T Ind Electron 66: 4468–4478. https://doi.org/10.1109/TIE.2018.2860535 doi: 10.1109/TIE.2018.2860535
|
| [23] |
Hassan M, Ge X, Woldegiorgis AT, Mastoi MS, Shahid MB, Atif R, et al. (2023) A look-up table-based model predictive torque control of IPMSM drives with duty cycle optimization. ISA T. https://doi.org/10.1016/j.isatra.2023.02.007 doi: 10.1016/j.isatra.2023.02.007
|
| [24] |
Xiong F, Wu J, Hao L, Liu Z (2017) Backflow Power Optimization Control for Dual Active Bridge DC-DC Converters. Energies 10: 1–27. https://doi.org/10.3390/en10091403 doi: 10.3390/en10091403
|
| [25] |
Tong A, Hang L, Li G, Jiang X, Gao S (2017) Modeling and Analysis of a DualActive-Bridge-Isolated Bidirectional DC/DC Converter to Minimize RMS Current With Whole Operating Range. IEEE T Power Electron 33: 5302–5316. https://doi.org/10.1109/TPEL.2017.2692276 doi: 10.1109/TPEL.2017.2692276
|
| [26] |
Jaalam N, Ahmad AZ, Khalid AM, Abdullah R, Saad NM, Ghani SA, et al. (2022) Low Voltage Ride through Enhancement Using Grey Wolf Optimizer to Reduce Overshoot Current in the Grid-Connected PV System. Math Probl Eng 2022: 3917775. https://doi.org/10.1155/2022/3917775 doi: 10.1155/2022/3917775
|
| [27] |
Salman SS, Humod AT, Hasan FA (2022) Optimum control for dynamic voltage restorer based on particle swarm optimization algorithm. Indonesian Journal of Electrical Engineering and Computer Science 26: 1351–1359. https://doi.org/10.11591/ijeecs.v26.i3.pp1351-1359 doi: 10.11591/ijeecs.v26.i3.pp1351-1359
|
| [28] |
Kumar R, Bansal HO, Gautam AR, Mahela OP, Khan B (2022) Experimental Investigations on Particle Swarm Optimization Based Control Algorithm for Shunt Active Power Filter to Enhance Electric Power Quality. IEEE Access 10: 54878–54890. https://doi.org/10.1109/ACCESS.2022.3176732 doi: 10.1109/ACCESS.2022.3176732
|
| [29] |
Borin LC, Mattos E, Osorio CR, Koch GG, Montagner VF (2019) Robust PID Controllers Optimized by PSO Algorithm for Power Converters. 2019 IEEE 15th Brazilian Power Electronics Conference and 5th IEEE Southern Power Electronics Conference, COBEP/SPEC, 1‒6. https://doi.org/10.1109/COBEP/SPEC44138.2019.9065642 doi: 10.1109/COBEP/SPEC44138.2019.9065642
|
| [30] |
Song W, Hou N, Wu M (2017) Virtual Direct Power Control Scheme of Dual Active Bridge DC – DC Converters for Fast Dynamic Response. IEEE T Power Electron 33: 1750–1759. https://doi.org/10.1109/TPEL.2017.2682982 doi: 10.1109/TPEL.2017.2682982
|
| [31] |
Hassan M, Ge X, Atif R, Woldegiorgis AT, Mastoi MS, Shahid MB (2022) Computational efficient model predictive current control for interior permanent magnet synchronous motor drives. IET Power Electron 15: 1111–1133. https://doi.org/10.1049/pel2.12294 doi: 10.1049/pel2.12294
|
| [32] |
Zeng D, Zheng Y, Luo W, Hu Y, Cui Q, Li Q, et al. (2019) Research on Improved Auto-Tuning of a PID Controller Based on Phase Angle Margin. Energies 12: 1–16. https://doi.org/10.3390/en12091704 doi: 10.3390/en12091704
|
| [33] |
Sun X, Qiu J (2021) A Customized Voltage Control Strategy for Electric Vehicles in Distribution Networks with Reinforcement Learning Method. IEEE T Ind Inform 17: 6852–6863. https://doi.org/10.1109/TII.2021.3050039 doi: 10.1109/TII.2021.3050039
|
| [34] |
Wang Y, Qiu D, Strbac G, Gao Z (2023) Coordinated Electric Vehicle Active and Reactive Power Control for Active Distribution Networks. IEEE T Ind Inform 19: 1611–1622. https://doi.org/10.1109/TII.2022.3169975 doi: 10.1109/TII.2022.3169975
|
| [35] |
Ab-Ghani S, Daniyal H, Jaalam N, Ramlan NH, Saad NM (2022) Time-Variant Online Auto-Tuned PI Controller Using PSO Algorithm for High Accuracy Dual Active Bridge DC-DC Converter. 2022 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS, 36‒41. https://doi.org/10.1109/I2CACIS54679.2022.9815470 doi: 10.1109/I2CACIS54679.2022.9815470
|
| [36] |
Liu H, Song Q, Zhang C, Chen J, Deng B, Li J (2019) Development of bi-directional DC / DC converter for fuel cell hybrid vehicle. J Renew Sustain Energy 11: 44303. https://doi.org/10.1063/1.5094512 doi: 10.1063/1.5094512
|
| [37] |
Yang J, Liu J, Zhang J, Zhao N, Wang Y, Zheng TQ (2018) Multirate Digital Signal Processing and Noise Suppression for Dual Active Bridge DC DC Converters in a Power Electronic Traction Transformer. IEEE T Power Electron 33: 10885–10902. https://doi.org/10.1109/TPEL.2018.2803744 doi: 10.1109/TPEL.2018.2803744
|
| [38] |
Rodriquez A, Vazquez A, Lamar DG, Hernando MM, Sebastia J (2014) Different Purpose Design Strategies and Techniques to Improve the Performance of a Dual Active Bridge With Phase-Shift Control. IEEE T Power Electron 30: 790–804. https://doi.org/10.1109/TPEL.2014.2309853 doi: 10.1109/TPEL.2014.2309853
|
| [39] | Kennedy J, Eberhart R (1995) Particle Swarm Optimization. Proc IEEE Int Conf Neural Networks 4: 1942–1948. |
| [40] |
Ishaque K, Salam Z, Shamsudin A, Amjad M (2012) A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm. Appl Energy 99: 414–422. https://doi.org/10.1016/j.apenergy.2012.05.026 doi: 10.1016/j.apenergy.2012.05.026
|
| [41] |
Ishaque K, Salam Z, Amjad M, Mekhilef S (2012) An Improved Particle Swarm Optimization (PSO)– Based MPPT for PV With Reduced Steady-State Oscillation. IEEE T Power Electron 27: 3627–3638. https://doi.org/10.1109/TPEL.2012.2185713 doi: 10.1109/TPEL.2012.2185713
|
Figures(13) / Tables(2)
Suliana Ab-Ghani, Hamdan Daniyal, Abu Zaharin Ahmad, Norazila Jaalam, Norhafidzah Mohd Saad, Nur Huda Ramlan, Norhazilina Bahari. Adaptive online auto-tuning using Particle Swarm optimized PI controller with time-variant approach for high accuracy and speed in Dual Active Bridge converter[J]. AIMS Electronics and Electrical Engineering, 2023, 7(2): 156-170. doi: 10.3934/electreng.2023009
| Components | Values |
| Transformer turns ratio, n | 2 :1 |
| Rated power, Pmax | 200 kW |
| Input voltage, Vi | 750 V |
| Output voltage, Vo | 500 V |
| Leakage inductance, Lk | 24 µH |
| Switching frequency, fs | 20 kHz |
DownLoad:
CSV
| Load Step-change | Vref (V) | Average eSS (%) | Average settling time (ms) | ||
| OPSO-PI-OT | OPSO-PI-RT | OPSO-PI-OT | OPSO-PI-RT | ||
| 100kW to 200kW | 270 | 1.47 | 1.39 | 15.80 | 15.60 |
| 375 | 1.44 | 1.36 | 13.70 | 13.20 | |
| 420 | 1.66 | 1.65 | 9.50 | 10.10 | |
| 150kW to 70kW | 270 | 4.13 | 3.20 | 10.50 | 10.05 |
| 375 | 1.54 | 1.57 | 9.90 | 10.80 | |
| 420 | 3.84 | 1.70 | 11.10 | 10.60 | |
DownLoad:
CSV
| Components | Values |
| Transformer turns ratio, n | 2 :1 |
| Rated power, Pmax | 200 kW |
| Input voltage, Vi | 750 V |
| Output voltage, Vo | 500 V |
| Leakage inductance, Lk | 24 µH |
| Switching frequency, fs | 20 kHz |
| Load Step-change | Vref (V) | Average eSS (%) | Average settling time (ms) | ||
| OPSO-PI-OT | OPSO-PI-RT | OPSO-PI-OT | OPSO-PI-RT | ||
| 100kW to 200kW | 270 | 1.47 | 1.39 | 15.80 | 15.60 |
| 375 | 1.44 | 1.36 | 13.70 | 13.20 | |
| 420 | 1.66 | 1.65 | 9.50 | 10.10 | |
| 150kW to 70kW | 270 | 4.13 | 3.20 | 10.50 | 10.05 |
| 375 | 1.54 | 1.57 | 9.90 | 10.80 | |
| 420 | 3.84 | 1.70 | 11.10 | 10.60 | |
